A new organizational nonlinear genetic algorithm for numerical optimization

Authors:
Zhihua Cui;Jianchao Zeng
Affiliations:
Division of system simulation and computer application, Taiyuan University of Science and Technology, Shanxi, P.R. China;Division of system simulation and computer application, Taiyuan University of Science and Technology, Shanxi, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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An improved multi-agent genetic algorithm for numerical optimization

Natural Computing: an international journal

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Abstract

Based on the concept of organization in economics, a novel genetic algorithm, organizational nonlinear genetic algorithm (ONGA), is proposed to solve global numerical optimization problems with continuous variables. In ONGA, genetic operators do not act on individuals directly, but on organizations, and four genetic operators,organization establish, organization classify, multi-parent crossover, and multi-parent mutation operators, are designed for organizations. Simulation results indicate that ONGA performs much better than the real-coded genetic algorithm both in the quality of solution and in the computational complexity.